{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Tutorial on how to create a denoising autoencoder w/ Tensorflow.\n",
    "\n",
    "Parag K. Mital, Jan 2016\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "from libs.utils import corrupt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %%\n",
    "def autoencoder(dimensions=[784, 512, 256, 64]):\n",
    "    \"\"\"Build a deep denoising autoencoder w/ tied weights.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    dimensions : list, optional\n",
    "        The number of neurons for each layer of the autoencoder.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    x : Tensor\n",
    "        Input placeholder to the network\n",
    "    z : Tensor\n",
    "        Inner-most latent representation\n",
    "    y : Tensor\n",
    "        Output reconstruction of the input\n",
    "    cost : Tensor\n",
    "        Overall cost to use for training\n",
    "    \"\"\"\n",
    "    # input to the network\n",
    "    x = tf.placeholder(tf.float32, [None, dimensions[0]], name='x')\n",
    "\n",
    "    # Probability that we will corrupt input.\n",
    "    # This is the essence of the denoising autoencoder, and is pretty\n",
    "    # basic.  We'll feed forward a noisy input, allowing our network\n",
    "    # to generalize better, possibly, to occlusions of what we're\n",
    "    # really interested in.  But to measure accuracy, we'll still\n",
    "    # enforce a training signal which measures the original image's\n",
    "    # reconstruction cost.\n",
    "    #\n",
    "    # We'll change this to 1 during training\n",
    "    # but when we're ready for testing/production ready environments,\n",
    "    # we'll put it back to 0.\n",
    "    corrupt_prob = tf.placeholder(tf.float32, [1])\n",
    "    current_input = corrupt(x) * corrupt_prob + x * (1 - corrupt_prob)\n",
    "\n",
    "    # Build the encoder\n",
    "    encoder = []\n",
    "    for layer_i, n_output in enumerate(dimensions[1:]):\n",
    "        n_input = int(current_input.get_shape()[1])\n",
    "        W = tf.Variable(\n",
    "            tf.random_uniform([n_input, n_output],\n",
    "                              -1.0 / math.sqrt(n_input),\n",
    "                              1.0 / math.sqrt(n_input)))\n",
    "        b = tf.Variable(tf.zeros([n_output]))\n",
    "        encoder.append(W)\n",
    "        output = tf.nn.tanh(tf.matmul(current_input, W) + b)\n",
    "        current_input = output\n",
    "    # latent representation\n",
    "    z = current_input\n",
    "    encoder.reverse()\n",
    "    # Build the decoder using the same weights\n",
    "    for layer_i, n_output in enumerate(dimensions[:-1][::-1]):\n",
    "        W = tf.transpose(encoder[layer_i])\n",
    "        b = tf.Variable(tf.zeros([n_output]))\n",
    "        output = tf.nn.tanh(tf.matmul(current_input, W) + b)\n",
    "        current_input = output\n",
    "    # now have the reconstruction through the network\n",
    "    y = current_input\n",
    "    # cost function measures pixel-wise difference\n",
    "    cost = tf.sqrt(tf.reduce_mean(tf.square(y - x)))\n",
    "    return {'x': x, 'z': z, 'y': y,\n",
    "            'corrupt_prob': corrupt_prob,\n",
    "            'cost': cost}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Basic test\n",
    "def test_mnist():\n",
    "    import tensorflow as tf\n",
    "    import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # %%\n",
    "    # load MNIST as before\n",
    "    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "    mean_img = np.mean(mnist.train.images, axis=0)\n",
    "    ae = autoencoder(dimensions=[784, 256, 64])\n",
    "\n",
    "    # %%\n",
    "    learning_rate = 0.001\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(ae['cost'])\n",
    "\n",
    "    # %%\n",
    "    # We create a session to use the graph\n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "\n",
    "    # %%\n",
    "    # Fit all training data\n",
    "    batch_size = 50\n",
    "    n_epochs = 10\n",
    "    for epoch_i in range(n_epochs):\n",
    "        for batch_i in range(mnist.train.num_examples // batch_size):\n",
    "            batch_xs, _ = mnist.train.next_batch(batch_size)\n",
    "            train = np.array([img - mean_img for img in batch_xs])\n",
    "            sess.run(optimizer, feed_dict={\n",
    "                ae['x']: train, ae['corrupt_prob']: [1.0]})\n",
    "        print(epoch_i, sess.run(ae['cost'], feed_dict={\n",
    "            ae['x']: train, ae['corrupt_prob']: [1.0]}))\n",
    "\n",
    "    # %%\n",
    "    # Plot example reconstructions\n",
    "    n_examples = 15\n",
    "    test_xs, _ = mnist.test.next_batch(n_examples)\n",
    "    test_xs_norm = np.array([img - mean_img for img in test_xs])\n",
    "    recon = sess.run(ae['y'], feed_dict={\n",
    "        ae['x']: test_xs_norm, ae['corrupt_prob']: [0.0]})\n",
    "    fig, axs = plt.subplots(2, n_examples, figsize=(10, 2))\n",
    "    for example_i in range(n_examples):\n",
    "        axs[0][example_i].imshow(\n",
    "            np.reshape(test_xs[example_i, :], (28, 28)))\n",
    "        axs[1][example_i].imshow(\n",
    "            np.reshape([recon[example_i, :] + mean_img], (28, 28)))\n",
    "    fig.show()\n",
    "    plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "(0, 0.14199062)\n",
      "(1, 0.13837112)\n",
      "(2, 0.1353873)\n",
      "(3, 0.13011663)\n",
      "(4, 0.13082016)\n",
      "(5, 0.12943956)\n",
      "(6, 0.13008749)\n",
      "(7, 0.12971884)\n",
      "(8, 0.12277839)\n",
      "(9, 0.12314773)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/heythisischo/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAB9CAYAAABtT12EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYGBgYGBgYJAl/h9l5bhPuDuSIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3d59e2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    test_mnist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
